Vehicle exterior environment detection apparatus

ABSTRACT

A vehicle exterior environment detection apparatus includes an image width calculator, a predicted distance calculator, and a relative distance calculator. The image width calculator calculates a first image width of a target vehicle on the basis of a first image. The predicted distance calculator calculates a first predicted distance to the target vehicle on the basis of the first image width. The relative distance calculator calculates a first reliability of the first image width, and, when the first reliability is higher than a predetermined threshold, calculates a first real width of the target vehicle on the basis of the first image width and the first predicted distance, updates a smoothed real width by performing smoothing processing on the basis of the first real width, and calculates a first distance to the target vehicle on the basis of the smoothed real width and the first image width.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims priority from Japanese Patent ApplicationNo. 2019-043516 filed on Mar. 11, 2019 and Japanese Patent ApplicationNo. 2019-160293 filed on Sep. 3, 2019, the entire contents of each ofwhich are hereby incorporated by reference.

BACKGROUND

The technology relates to a vehicle exterior environment detectionapparatus that detects a vehicle around an own vehicle.

There are some vehicles such as automobiles each of which detects avehicle around an own vehicle and controls, for example, traveling ofthe own vehicle depending on a result obtained by the detection. Forexample, Japanese Unexamined Patent Application Publication No.2008-123462 discloses a technique that detects a vehicle around an ownvehicle using a stereo camera, and calculates, on the basis of a widthof the relevant vehicle, a relative speed between a traveling speed ofthe own vehicle and a traveling speed of the relevant vehicle.

SUMMARY

An aspect of the technology provides a vehicle exterior environmentdetection apparatus that includes an image width calculator, a predicteddistance calculator, and a relative distance calculator. The image widthcalculator is configured to calculate a first image width of a targetvehicle on the basis of a first image. The first image is one of a leftimage and a right image. The predicted distance calculator is configuredto calculate a first predicted distance to the target vehicle on thebasis of the first image width. The relative distance calculator isconfigured to calculate a first reliability of the first image width,and, when the first reliability is higher than a predeterminedthreshold, configured to calculate a first real width of the targetvehicle on the basis of the first image width and the first predicteddistance, update a smoothed real width by performing smoothingprocessing on the basis of the first real width, and calculate a firstdistance to the target vehicle on the basis of the smoothed real widthand the first image width.

An aspect of the technology provides a vehicle exterior environmentdetection apparatus including circuitry. The circuitry is configured tocalculate a first image width of a target vehicle on the basis of afirst image, the first image being one of a left image and a rightimage, calculate a first predicted distance to the target vehicle on thebasis of the first image width, calculate a first reliability of thefirst image width, and determine whether the first reliability is higherthan a predetermined threshold. When the first reliability is higherthan a predetermined threshold, the circuitry is configured to calculatea first real width of the target vehicle on the basis of the first imagewidth and the first predicted distance, update a smoothed real width byperforming smoothing processing on the basis of the first real width,and calculate a first distance to the target vehicle on the basis of thesmoothed real width and the first image width.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings are included to provide a furtherunderstanding of the disclosure and are incorporated in and constitute apart of this specification. The drawings illustrate example embodimentsand, together with the specification, serve to explain the principles ofthe technology.

FIG. 1 is a block diagram illustrating a configuration example of avehicle exterior environment detection apparatus according to oneexample embodiment of the technology.

FIG. 2A is an image diagram illustrating an example of a left imagegenerated by a left camera illustrated in FIG. 1.

FIG. 2B is an image diagram illustrating an example of a right imagegenerated by a right camera illustrated in FIG. 1.

FIG. 3 is an image diagram schematically illustrating an operationexample of a preceding vehicle detector illustrated in FIG. 1.

FIG. 4 is an image diagram schematically illustrating an operationexample of another preceding vehicle detector illustrated in FIG. 1.

FIG. 5 is an image diagram schematically illustrating another operationexample of the other preceding vehicle detector illustrated in FIG. 1.

FIG. 6 is a flowchart illustrating an example of a traveling datadetector illustrated in FIG. 1.

FIG. 7A is a characteristic diagram illustrating an example of arelative distance.

FIG. 7B is a characteristic diagram illustrating an example of atraveling speed of a preceding vehicle.

FIG. 8A is a characteristic diagram illustrating an example of apredicted relative distance.

FIG. 8B is a characteristic diagram illustrating an example of apredicted traveling speed of a preceding vehicle.

DETAILED DESCRIPTION

In the following, some example embodiments of the technology aredescribed with reference to the accompanying drawings. Note that thefollowing description is directed to illustrative examples of thedisclosure and not to be construed as limiting to the technology. Ineach of the drawings referred to in the following description, elementshave different scales in order to illustrate the respective elementswith sizes recognizable in the drawings. Therefore, factors including,without limitation, the number of each of the elements, the shape ofeach of the elements, a size of each of the elements, a ratio betweenthe elements, and relative positional relationship between the elementsare illustrative only and not to be construed as limiting to thetechnology. Further, elements in the following example embodiments whichare not recited in a most-generic independent claim of the disclosureare optional and may be provided on an as-needed basis. Throughout thepresent specification and the drawings, elements having substantiallythe same function and configuration are denoted with the same numeralsto avoid any redundant description.

In processing of detecting a vehicle around an own vehicle, it isdesired that detection accuracy be high, and a further improvement inthe detection accuracy is expected.

It is desirable to provide a vehicle exterior environment detectionapparatus that is able to enhance accuracy of detecting a vehicle.

Example Embodiment Configuration Example

FIG. 1 illustrates a configuration example of a vehicle exteriorenvironment detection apparatus 1 according to an example embodiment. Inone embodiment, the vehicle exterior environment detection apparatus 1may serve as a “vehicle exterior environment detection apparatus”. Thevehicle exterior environment detection apparatus 1 may include a stereocamera 11 and a processor 20. The vehicle exterior environment detectionapparatus 1 may be mounted on a vehicle 10 such as an automobile.

The stereo camera 11 may capture an image ahead of the vehicle 10 tothereby generate a pair of images (a left image PL and a right image PR)each having a parallax with respect to each other. The stereo camera 11may have a left camera 11L and a right camera 11R. In this example, theleft camera 11L and a right camera 11R may be disposed in the vicinityof a rearview mirror of the vehicle 10 and separated away from eachother by a predetermined distance in a width direction of the vehicle10. The left camera 11L and the right camera 11R may perform an imagingoperation synchronously. The left camera 11L may generate the left imagePL, and the right camera 11R may generate the right image PR. The leftimage PL and the right image PR may form a stereo image PIC. The stereocamera 11 may perform the imaging operation at a predetermined framerate (for example, 60 [fps]) to thereby generate a series of stereoimages PIC.

FIG. 2A illustrates an example of the left image PL, and FIG. 2Billustrates an example of the right image PR. In this example, anothervehicle, i.e., a preceding vehicle 90, is traveling ahead of the vehicle10 on a road on which the vehicle 10 is traveling. The left camera 11Lmay capture an image of the preceding vehicle 90 to thereby generate theleft image PL, and the right camera 11R may capture an image of thepreceding vehicle 90 to thereby generate the right image PR. As aresult, the left image PL and the right image PR may each have aparallax with respect to each other. The stereo camera 11 may generatethe stereo image PIC including the left image PL and the right image PR.

The processor 20 (illustrated in FIG. 1) may detect traveling data ofthe preceding vehicle 90 on the basis of the stereo image PIC suppliedby the stereo camera 11. The traveling data of the preceding vehicle 90may include, for example, a relative distance from the vehicle 10 to thepreceding vehicle 90, and a relative speed between a traveling speed ofthe vehicle 10 and a traveling speed of the preceding vehicle 90. Thevehicle 10 may perform, for example, traveling controls such as anadaptive cruise control and a steering-assisting control on the basis ofthe traveling data of the preceding vehicle 90 obtained by the processor20. The processor 20 may include, for example, a central processing unit(CPU) that executes programs, a random access memory (RAM) thattemporarily stores processing data, and a read only memory (ROM) thatstores programs. The processor 20 may include a distance image generator21, a preceding vehicle detector 22, a traveling data detector 23, animage accuracy detector 24, an image selector 25, a preceding vehicledetector 26, an own vehicle traveling data acquisition unit 27, atraveling data detector 30, and a traveling data determination unit 28.

The distance image generator 21 may perform predetermined imageprocessing including stereo matching processing and filtering processingon the basis of the left image PL and the right image PR included in thestereo image PIC, to thereby generate a distance image PZ. A pixel valueof each pixel in the distance image PZ may be a depth value in athree-dimensional real space, which indicates a distance to a pointcorresponding to the relevant pixel. The distance image generator 21 maysupply the preceding vehicle detector 22 with the generated distanceimage PZ.

The preceding vehicle detector 22 may detect the preceding vehicle 90 onthe basis of the distance image PZ. In the distance image PZ, depthvalues in an image region corresponding to the preceding vehicle 90 maybe smaller than depth values in an image region other than the imageregion corresponding to the preceding vehicle 90. The preceding vehicledetector 22 may detect the preceding vehicle 90 by using such depthvalues included in the distance image PZ.

FIG. 3 schematically illustrates an example of a detection resultobtained by the preceding vehicle detector 22. The preceding vehicledetector 22 may detect the preceding vehicle 90 on the basis of thedistance image PZ. Further, the preceding vehicle detector 22 mayidentify a position of the preceding vehicle 90 in the distance image PZas indicated by a region R1.

The traveling data detector 23 (illustrated in FIG. 1) may obtain, onthe basis of the distance image PZ, traveling data of the precedingvehicle 90 detected by the preceding vehicle detector 22. The travelingdata detector 23 may obtain the traveling data of the preceding vehicle90 by using the depth values in the image region corresponding to thepreceding vehicle 90 included in the distance image PZ.

The image accuracy detector 24 may detect image accuracy of the distanceimage PZ. One reason for detecting the image accuracy may be as follows.The distance image generator 21 may generate the distance image PZ onthe basis of the left image PL and the right image PR; and therefore, ina case where either one of the left image PL and the right image PR isunclear due to raindrops or backlight, for example, the image accuracyof the distance image PZ may decrease. In this case, there is apossibility that the position of the preceding vehicle 90 detected bythe preceding vehicle detector 22 and the traveling data obtained by thetraveling data detector 23 can be inaccurate. Accordingly, the imageaccuracy detector 24 may detect the image accuracy of the distance imagePZ on the basis of the distance image PZ. Further, the image accuracydetector 24 may supply the traveling data detector 30 and the travelingdata determination unit 28 with data related to the detection result.

The image selector 25 may select either one of the left image PL and theright image PR. In one example, the image selector 25 may evaluate, byusing a machine learning technique, a certainty that the precedingvehicle 90 is a vehicle on the basis of each of an image correspondingto the preceding vehicle 90 in the left image PL and an imagecorresponding to the preceding vehicle 90 in the right image PR, tothereby generate respective scores of the left image PL and the rightimage PR. The image selector 25 may select, as an image P, an imagewhose score is higher among the left image PL and the right image PR,and supply the preceding vehicle detector 26 with the selected image P.

The preceding vehicle detector 26 may detect the preceding vehicle 90 onthe basis of the image P. The preceding vehicle detector 26 may have, inthis example, a plurality of detection modes M for detecting thepreceding vehicle 90. The preceding vehicle detector 26 may select oneof the detection modes M on the basis of an environment condition, forexample, and detect the preceding vehicle 90 using the selecteddetection mode M.

In one example, under a condition in which the body of the precedingvehicle 90 is easily detected, such as during daytime hours, thepreceding vehicle detector 26 may select a detection mode M1 among thedetection modes M. In the detection mode M1, the preceding vehicledetector 26 may search for the preceding vehicle 90 on the basis of theimage P using a machine learning technique, to thereby detect thepreceding vehicle 90.

FIG. 4 schematically illustrates an example of processing in thedetection mode M1 performed by the preceding vehicle detector 26. Thepreceding vehicle detector 26 may search for the preceding vehicle 90 bysequentially setting processing target regions R2, slightly changing thepositions of the processing target regions R2 each time. The precedingvehicle detector 26 may confirm whether the preceding vehicle 90 existsin each processing target region R2 using a machine learning technique.In this way, the preceding vehicle detector 26 may identify the positionof the preceding vehicle 90 in the image P.

Further, for example, under a condition in which it is difficult todetect the body of the preceding vehicle 90, such as during nighttimehours, the preceding vehicle detector 26 may select a detection mode M2among the detection modes M. In the detection mode M2, the precedingvehicle detector 26 may detect taillights of the preceding vehicle 90 onthe basis of the image P, to thereby detect the preceding vehicle 90.

FIG. 5 schematically illustrates an example of processing in thedetection mode M2 performed by the preceding vehicle detector 26. Duringnighttime hours, the preceding vehicle 90 may travel with outputtinglight from a left taillight 91L and a right taillight 91R. Accordingly,even under a condition in which it is difficult to detect the body ofthe preceding vehicle 90, it is possible for the preceding vehicledetector 26 to detect the left taillight 91L and the right taillight91R. The preceding vehicle detector 26 may detect the left taillight 91Land the right taillight 91R in the image P to thereby identify theposition of the preceding vehicle 90 in the image P.

The own vehicle traveling data acquisition unit 27 (illustrated inFIG. 1) may acquire traveling data of the vehicle 10 that is an ownvehicle on which the vehicle exterior environment detection apparatus 1is mounted, on the basis of a detection signal from an unillustratedsensor included in the vehicle 10 or a control signal from anunillustrated control device included in the vehicle 10. The travelingdata of the vehicle 10 may include data related to a traveling speed V10of the vehicle 10, a yaw rate of the vehicle 10, a movement amount in avehicle-width direction (x-direction) of the vehicle 10, a movementamount in a vehicle-length direction (z-direction) of the vehicle 10,etc.

The traveling data detector 30 may obtain, on the basis of the image P,traveling data of the preceding vehicle 90 detected by the precedingvehicle detector 26. In one example, the traveling data detector 30 maycalculate an image width Wpic of the preceding vehicle 90 in the imageP, and, on the basis of a size of the image width Wpic, may calculate arelative speed V between a traveling speed of the vehicle 10 and atraveling speed of the preceding vehicle 90 and a relative distance Z tothe preceding vehicle 90, to thereby obtain the traveling data of thepreceding vehicle 90. That is, for example, in a case where a distancebetween the vehicle 10 and the preceding vehicle 90 is small, the imagewidth Wpic of the preceding vehicle 90 in the image P is large, and in acase where the distance between the vehicle 10 and the preceding vehicle90 is large, the image width Wpic of the preceding vehicle 90 in theimage P is small; therefore, the traveling data detector 30 is able toobtain the traveling data of the preceding vehicle 90 by using such animage size (scaling) of the preceding vehicle 90 in the image P. Thetraveling data detector 30 may include an image width calculator 31, apredicted distance calculator 32, a reliability determination unit 33, arelative distance calculator 34, and a relative speed calculator 35.

The image width calculator 31 calculates the image width Wpic of thepreceding vehicle 90 on the basis of the image P. For example, under acondition in which the body of the preceding vehicle 90 is easilydetected, such as during daytime hours, the image width calculator 31may calculate, as illustrated in FIG. 4, a vehicle width of thepreceding vehicle 90 in the image P as the image width Wpic. Further,for example, under a condition in which it is difficult to detect thebody of the preceding vehicle 90, such as during nighttime hours, theimage width calculator 31 may calculate, as illustrated in FIG. 5, adistance between the center of the left taillight 91L and the center ofthe right taillight 91R in the image P as the image width Wpic.

The predicted distance calculator 32 calculates a predicted relativedistance Zpre to the preceding vehicle 90 on the basis of the imagewidth Wpic calculated by the image width calculator 31. The predicteddistance calculator 32 may calculate a predicted relative distance Zpreto the preceding vehicle 90 on the basis of the image width Wpiccalculated by the image width calculator 31 and the traveling data ofthe vehicle 10 supplied by the own vehicle traveling data acquisitionunit 27.

The reliability determination unit 33 evaluates a reliability of theimage width Wpic calculated by the image width calculator 31. In oneexample, the reliability determination unit 33 calculates thereliability of the image width Wpic on the basis of the distance imagePZ, and compares the calculated reliability of the image width Wpic witha predetermined threshold.

The relative distance calculator 34 calculates the relative distance Zon the basis of the image width Wpic calculated by the image widthcalculator 31 and the predicted relative distance Zpre calculated by thepredicted distance calculator 32. In one example, in a case where thereliability of the image width Wpic calculated by the reliabilitydetermination unit 33 is higher than the predetermined threshold, therelative distance calculator 34 calculates an actual width (real widthWreal) corresponding to the image width Wpic of the preceding vehicle90, on the basis of the predicted relative distance Zpre calculated bythe predicted distance calculator 32 and the image width Wpic. Further,the relative distance calculator 34 updates a real width Wreal1 byperforming smoothing processing on the basis of the real width Wreal.Moreover, the relative distance calculator 34 calculates the relativedistance Z on the basis of the real width Wreal1 and the image widthWpic.

The relative speed calculator 35 may calculate the relative speed V onthe basis of the relative distance Z calculated by the relative distancecalculator 34.

In this way, the traveling data detector 30 is able to obtain thetraveling data of the preceding vehicle 90 on the basis of the image P.

The traveling data determination unit 28 may determine the travelingdata of the preceding vehicle 90 on the basis of, depending on adetection result obtained by the image accuracy detector 24, thetraveling data of the preceding vehicle 90 based on the distance imagePZ that is obtained by the traveling data detector 23 and the travelingdata of the preceding vehicle 90 based on the image P that is obtainedby the traveling data detector 30. In one example, in a case where theimage accuracy of the distance image PZ is high, the traveling datadetermination unit 28 may determine the traveling data of the precedingvehicle 90 on the basis of the traveling data of the preceding vehicle90 that is obtained by the traveling data detector 23 on the basis ofthe distance image PZ, and in a case where the image accuracy of thedistance image PZ is low, the traveling data determination unit 28 maydetermine the traveling data of the preceding vehicle 90 on the basis ofthe traveling data of the preceding vehicle 90 that is obtained by thetraveling data detector 30 on the basis of the image P.

With such a configuration, in the vehicle exterior environment detectionapparatus 1, the traveling data detector 23 may continuously obtain thetraveling data of the preceding vehicle 90 on the basis of a series ofdistance images PZ generated on the basis of the series of stereo imagesPIC, and the traveling data detector 30 may also continuously obtain thetraveling data of the preceding vehicle 90 on the basis of a series ofimages P included in the series of stereo images PIC. At that time, in acase where either one of the left image PL and the right image PRbecomes unclear due to raindrops, etc., and the image accuracy of thedistance image PZ decreases, for example, the vehicle exteriorenvironment detection apparatus 1 may determine the traveling data ofthe preceding vehicle 90 on the basis of the traveling data of thepreceding vehicle 90 that is obtained on the basis of an image (imageP), which is either one of the left image PL and the right image PR thathas a higher certainty that the preceding vehicle 90 is a vehicle. Inthis way, the vehicle exterior environment detection apparatus 1 is ableto enhance accuracy of detecting the preceding vehicle 90.

In one embodiment, the image width calculator 31 may serve as an “imagewidth calculator”. In one embodiment, the predicted distance calculator32 may serve as a “predicted distance calculator”. In one embodiment,the reliability determination unit 33 and the relative distancecalculator 34 may serve as a “relative distance calculator”. In oneembodiment, the relative speed calculator 35 may serve as a “relativespeed calculator”. In one embodiment, the preceding vehicle 90 may serveas a “target vehicle”.

Operations and Workings

Now, description will be given on operations and workings of the vehicleexterior environment detection apparatus 1 of the example embodiment.

Outline of Overall Operations

First, with reference to FIG. 1, an outline of overall operations of thevehicle exterior environment detection apparatus 1 will be described.The stereo camera 11 may capture an image ahead of the vehicle 10 tothereby generate the stereo image PIC including the left image PL andthe right image PR each having a parallax with respect to each other.The distance image generator 21 may generate the distance image PZ onthe basis of the left image PL and the right image PR included in thestereo image PIC. The preceding vehicle detector 22 may detect thepreceding vehicle 90 on the basis of the distance image PZ. Thetraveling data detector 23 may obtain, on the basis of the distanceimage PZ, the traveling data of the preceding vehicle 90 detected by thepreceding vehicle detector 22. The image accuracy detector 24 may detectthe image accuracy of the distance image PZ. The image selector 25 mayevaluate, by using a machine learning technique, the certainty that thepreceding vehicle 90 is a vehicle on the basis of each of the left imagePL and the right image PR, to thereby select one of the left image PLand the right image PR as the image P. The preceding vehicle detector 26may detect the preceding vehicle 90 on the basis of the image P. The ownvehicle traveling data acquisition unit 27 may acquire the travelingdata of the vehicle 10 on the basis of a detection signal from anunillustrated sensor included in the vehicle 10 or a control signal froman unillustrated control device included in the vehicle 10. Thetraveling data detector 30 may obtain, on the basis of the image P, thetraveling data of the preceding vehicle 90 detected by the precedingvehicle detector 26. The traveling data determination unit 28 maydetermine the traveling data of the preceding vehicle 90 on the basisof, depending on a detection result obtained by the image accuracydetector 24, the traveling data of the preceding vehicle 90 based on thedistance image PZ that is obtained by the traveling data detector 23 andthe traveling data of the preceding vehicle 90 based on the image P thatis obtained by the traveling data detector 30.

Detailed Operations

FIG. 6 illustrates an example of operations performed by the travelingdata detector 30. The traveling data detector 30 may perform thefollowing operations on each of a series of frames F.

First, the image width calculator 31 included in the traveling datadetector 30 calculates the image width Wpic of the preceding vehicle 90on the basis of the image P (step S101). For example, under a conditionin which the body of the preceding vehicle 90 is easily detected, suchas during daytime hours, the image width calculator 31 may calculate, asillustrated in FIG. 4, a vehicle width of the preceding vehicle 90 inthe image P as the image width Wpic. Further, for example, under acondition in which it is difficult to detect the body of the precedingvehicle 90, such as during nighttime hours, the image width calculator31 may calculate, as illustrated in FIG. 5, a distance between thecenter of the left taillight 91L and the center of the right taillight91R in the image P as the image width Wpic.

Thereafter, the predicted distance calculator 32 included in thetraveling data detector 30 calculates a predicted relative distance Zpreto the preceding vehicle 90 on the basis of the image width Wpiccalculated in step S101. The predicted distance calculator 32 maycalculate a predicted relative distance Zpre to the preceding vehicle 90on the basis of the image width Wpic calculated in step S101 and thetraveling data of the vehicle 10 supplied by the own vehicle travelingdata acquisition unit 27 (step S102).

In one example, the predicted distance calculator 32 may calculate, onthe basis of the image width Wpic, a predicted relative speed Vprebetween the traveling speed of the vehicle 10 and the traveling speed ofthe preceding vehicle 90 by using the following Equation (1) and theimage size (scaling) of the preceding vehicle 90.

$\begin{matrix}{{{Vpre}(n)} = {\frac{{{Wpic}\left( {n - 1} \right)} - {{Wpic}(n)}}{{Wpic}(n)} \cdot \frac{Z\left( {n - 1} \right)}{\Delta t}}} & (1)\end{matrix}$

Where: Vpre(n) represents a predicted relative speed regarding an n-thframe F; Wpic(n) represents an image width obtained from an image Pregarding the n-th frame F, and Wpic(n−1) represents an image widthobtained from an image P regarding an (n−1)th frame F; Z(n−1) representsa relative distance regarding the (n−1)th frame F; and Δt represents areciprocal of a frame rate, and, for example, in a case where the framerate is 60 [fps], Δt is 16.7 [msec] (= 1/60).

Thereafter, the predicted distance calculator 32 may calculate apredicted traveling speed V90pre of the preceding vehicle 90 on thebasis of the predicted relative speed Vpre and on the basis of thetraveling speed V10 of the vehicle 10 supplied by the own vehicletraveling data acquisition unit 27, by using the following Equation (2).V90pre(n)=V10(n)+Vpre(n)  (2)

Where: V90pre(n) represents a predicted traveling speed of the precedingvehicle 90 regarding the n-th frame F; and V10(n) represents a travelingspeed of the vehicle 10 regarding the n-th frame F.

Further, the predicted distance calculator 32 may calculate thepredicted relative distance Zpre to the preceding vehicle 90 on thebasis of: a predicted traveling speed V90pre(n−1) of the precedingvehicle 90 regarding the (n−1)th frame F; a relative distance Z(n−1)regarding the (n−1)th frame F; a movement amount of the vehicle 10 inthe vehicle-width direction (x-direction) of the vehicle 10 at time Δt;a movement amount of the vehicle 10 in the vehicle-length direction(z-direction) of the vehicle 10 at time Δt; a yaw rate of the vehicle10; etc.

In this way, the predicted distance calculator 32 may calculate thepredicted relative distance Zpre.

Thereafter, the traveling data detector 30 may confirm the imageaccuracy of the distance image PZ on the basis of a detection resultobtained by the image accuracy detector 24 (step S103). In a case wherethe image accuracy of the distance image PZ is low (“N” in step S103),the processing may proceed to step S108.

In a case where the image accuracy of the distance image PZ is high instep S103 (“Y” in step S103), the reliability determination unit 33calculates the reliability of the image width Wpic calculated in stepS101 (step S104), and compares the calculated reliability with thepredetermined threshold (step S105).

In one example, in a case where the preceding vehicle detector 26operates in the detection mode M2, the reliability determination unit 33may calculate, on the basis of the distance image PZ, a parameterrelated to the left taillight 91L and a parameter related to the righttaillight 91R, and calculate a difference between those parameters, tothereby calculate the reliability. In one example, the reliabilitydetermination unit 33 may detect: a difference between a relativedistance to the left taillight 91L and a relative distance to the righttaillight 91R; a difference between the area of the left taillight 91Land the area of the right taillight 91R; a difference between a heightof a position of the left taillight 91L and a height of a position ofthe right taillight 91R; a difference between a width of the lefttaillight 91L and a width of the right taillight 91R; and a differencebetween a vertical length of the left taillight 91L and a verticallength of the right taillight 91R. Thereafter, the reliabilitydetermination unit 33 may calculate the reliability of the image widthWpic on the basis of those five differences. With decrease in the valueof each of the five differences, the reliability of the image width Wpicmay increase. In other words, the reliability determination unit 33 maycalculate the reliability of the image width Wpic by evaluating whetherthe vehicle 10 is located at a position right behind the precedingvehicle 90 and around the relevant position. For example, thereliability of the image width Wpic may increase as the vehicle 10 comescloser to the position right behind the preceding vehicle 90.Thereafter, the reliability determination unit 33 compares thecalculated reliability of the image width Wpic with the predeterminedthreshold.

For example, in a case where the preceding vehicle 90 travels diagonallyin front of the vehicle 10, the difference between the relative distanceto the left taillight 91L and the relative distance to the righttaillight 91R, for example, becomes large; therefore, the precedingvehicle detector 26 may determine that the reliability of the imagewidth Wpic is low. That is, in the a where the preceding vehicle 90travels diagonally in front of the vehicle 10 as in this case, thedistance between the center of the left taillight 91L and the center ofthe right taillight 91R indicated by the image width Wpic calculated instep S101 is apparently smaller than an actual distance; therefore, theimage width Wpic can take an inaccurate value. Accordingly, thereliability determination unit 33 may determine that the reliability ofthe image width Wpic is low.

In step S105, in a case where the reliability of the image width Wpic islower than the predetermined threshold (“N” in step S105), theprocessing may proceed to step S108.

In step S105, in a case where the reliability of the image width Wpic ishigher than the predetermined threshold (“Y” in step S105), the relativedistance calculator 34 calculates the actual width (real width Wreal)corresponding to the image width Wpic of the preceding vehicle 90 on thebasis of the image width Wpic calculated in step S101 and the predictedrelative distance Zpre calculated in step S102 (step S106). Thiscalculation may utilize a known calculation method using the image size(scaling) of the preceding vehicle 90, for example.

Thereafter, the relative distance calculator 34 updates a real widthWreal1 by performing smoothing processing on the basis of the real widthWreal calculated in step S106 (step S107). That is, a series of realwidths Wreal may be sequentially calculated in step S106 on the basis ofa series of images P; therefore, the relative distance calculator 34 mayperform the smoothing processing on the basis of the series of realwidths Wreal every time the real width Wreal is calculated in step S106,to thereby update the real width Wreal1. In one example, the relativedistance calculator 34 may perform the smoothing processing by using thefollowing Equation (3).

$\begin{matrix}{{{Wreal}\; 1(n)} = \frac{{{Wreal}\; 1\left( {n - 1} \right) \times {Areal}\; 1} + {{{Wrea1}(n)} \times {Area1}}}{{{Areal}\; 1} + {Areal}}} & (3)\end{matrix}$

Where: Wreal1(n) represents a real width after the smoothing processingregarding the n-th frame F; Wreal1(n−1) represents a real width afterthe smoothing processing regarding the (n−1)th frame F; Wreal(n)represents a real width before the smoothing processing regarding then-th frame F; Areal1 represents a weighting coefficient for the realwidth Wreal1(n−1) after the smoothing processing; and Areal represents aweighting coefficient for the real width Wreal(n) before the smoothingprocessing. The weighting coefficient Areal1 is a coefficient thatvaries, gradually increases as the number of times the smoothingprocessing is performed increases, and is set to reach a predeterminedupper limit in the end. Thus, in a case where the number of times thesmoothing processing is performed is small, the value of the real widthWreal1 can go up and down and fluctuate; however, as the number of timesthe smoothing processing is performed increases to a certain extent, thevalue of the real width Wreal1 is smoothed and prevented from deviatinglargely from the last value. In this way, the relative distancecalculator 34 may perform the smoothing processing to thereby update thereal width Wreal1.

Thereafter, the relative distance calculator 34 calculates the relativedistance Z on the basis of the image width Wpic calculated in step S101and the real width Wreal1 calculated by the smoothing processing in stepS107 (step S108). This calculation may utilize a known calculationmethod using the image size (scaling) of the preceding vehicle 90, forexample. For example, in a case where the processing proceeds directlyfrom step S103 or step S105 to step S108 (“N” in step S103 or S105), thereal width Wreal1 may not necessarily be updated in step S107.Accordingly, the relative distance calculator 34 may calculate therelative distance Z on the basis of the image width Wpic calculated instep S101 and the latest real width Wreal1 that has been updated in thepast.

Thereafter, the relative speed calculator 35 may calculate the relativespeed V on the basis of the relative distance Z calculated in step S108(step S109). In one example, the relative speed calculator 35 maycalculate the relative speed V on the basis of the relative distance Zby using the following Equation (4).

$\begin{matrix}{{V(n)} = \frac{{Z(n)} - {Z\left( {n - 1} \right)}}{\Delta\; t}} & (4)\end{matrix}$

Where: Z(n) represents a relative distance regarding the n-th frame F;and Z(n−1) represents a relative distance regarding the (n−1)th frame F.The traveling data detector 30 may calculate a traveling speed V90 ofthe preceding vehicle 90 on the basis of the relative speed V, by usingthe following Equation (5).V90(n)=V10(n)+V(n)  (5)

Where V90(n) represents a traveling speed of the preceding vehicle 90regarding the n-th frame F.

This may be the end of this flow.

In this way, the vehicle exterior environment detection apparatus 1 mayobtain the traveling data of the preceding vehicle 90 on the basis ofthe distance image PZ and may also detect the traveling data of thepreceding vehicle 90 on the basis of the image P, and is therefore ableto enhance accuracy of detecting the preceding vehicle 90. That is, in acase where either one of the left image PL and the right image PR isunclear due to raindrops or backlight, for example, the image accuracyof the distance image PZ can decrease, and as a result, there is apossibility that the accuracy of the traveling data of the precedingvehicle 90 detected on the basis of the distance image PZ can be low.However, the vehicle exterior environment detection apparatus 1 mayobtain the traveling data of the preceding vehicle 90 on the basis ofthe distance image PZ and may also obtain the traveling data of thepreceding vehicle 90 on the basis of the image P. Therefore, even in thecase where either one of the left image PL and the right image PR isunclear as mentioned above, the vehicle exterior environment detectionapparatus 1 is able to obtain the traveling data of the precedingvehicle 90 on the basis of the image (image P) which is the clearer oneof the left image PL and the right image PR. Thus, it is possible toenhance the accuracy of detecting the preceding vehicle 90.

Further, in the vehicle exterior environment detection apparatus 1, thereliability determination unit 33 included in the traveling datadetector 30 calculates the reliability of the image width Wpic. In acase where the reliability is higher than the predetermined threshold,the relative distance calculator 34 calculates the real width Wreal onthe basis of the image width Wpic and the predicted relative distanceZpre, updates the real width Wreal1 by performing smoothing processingon the basis of the real width Wreal, and calculates the relativedistance Z on the basis of the real width Wreal1 and the image widthWpic. With this configuration, the vehicle exterior environmentdetection apparatus 1 is able to enhance accuracy of the relativedistance Z as described below.

FIG. 7A illustrates an example of a series of relative distances Zcalculated by the traveling data detector 30, and FIG. 7B illustrates anexample of the traveling speed V90 of the preceding vehicle 90calculated by the traveling data detector 30. In this example, asillustrated in FIG. 7B, the preceding vehicle 90 is traveling at anapproximately constant speed during a time period before timing t1. Thevehicle 10 is traveling at a traveling speed lower than the travelingspeed of the preceding vehicle 90, and, as illustrated in FIG. 7A, therelative distance Z gradually increases as the time elapses. Thereafter,as illustrated in FIG. 7B, the preceding vehicle 90 starts todeaccelerate at timing t1. In response to this, as illustrated in FIG.7A, the relative distance Z starts to decrease at timing t1. Thereafter,the preceding vehicle 90 stops at timing t2.

FIG. 8A illustrates an example of a series of predicted relativedistances Zpre, and FIG. 8B illustrates an example of the predictedtraveling speed V90pre of the preceding vehicle 90. The predictedrelative distance Zpre illustrated in FIG. 8A is similar to the relativedistance Z illustrated in FIG. 7A, and the predicted traveling speedV90pre illustrated in FIG. 8B is similar to the traveling speed V90illustrated in FIG. 7B. The predicted relative distance Zpre (FIG. 8A)differs from the relative distance Z (FIG. 7A) in that, for example,fluctuations occur in parts A and B. The fluctuations in the predictedrelative distance Zpre are each attributed to a fluctuation in thepredicted relative speed Vpre at the time of calculating the predictedrelative speed Vpre on the basis of the image width Wpic by usingEquation (1). The traveling data detector 30 may perform, on the basisof such a predicted relative speed Vpre, the calculations indicated insteps S103 to S108 illustrated in FIG. 6, to thereby prevent thefluctuations like the fluctuations in predicted relative distance Zpre(FIG. 8A) from occurring in the relative distance Z (FIG. 7A).

That is, in the vehicle exterior environment detection apparatus 1, asillustrated in FIG. 6, in a case where the reliability of the imagewidth Wpic is higher than the predetermined threshold (“Y” in stepS105), the relative distance calculator 34 calculates the real widthWreal on the basis of the image width Wpic and the predicted relativedistance Zpre, updates the real width Wreal1 by performing the smoothingprocessing on the basis of the real width Wreal, and calculates therelative distance Z on the basis of the real width Wreal1 and the imagewidth Wpic. Further, in a case where the reliability of the image widthWpic is lower than the predetermined threshold (“N” in step S105), thevehicle exterior environment detection apparatus 1 may not necessarilyupdate the real width Wreal1 and may calculate the relative distance Zon the basis of the image width Wpic and the latest real width Wreal1that has been updated in the past. For example, in a case where thereliability of the image width Wpic is low, the predicted relative speedVpre can fluctuate. In the vehicle exterior environment detectionapparatus 1, the real width Wreal1 may not necessarily be updated in acase where the reliability of the image width Wpic is low; therefore, itis possible to reduce the possibility that the image width Wpic thatcauses the predicted relative speed Vpre to fluctuate influences thereal width Wreal1. In this manner, the vehicle exterior environmentdetection apparatus 1 is able to prevent the fluctuations like thefluctuations in predicted relative distances Zpre (FIG. 8A) fromoccurring in the relative distances Z (FIG. 7A). As a result, thevehicle exterior environment detection apparatus 1 is able to enhanceaccuracy of detecting the preceding vehicle 90.

Further, in the vehicle exterior environment detection apparatus 1, therelative distance calculator 34 included in the traveling data detector30 updates the real width Wreal1 by performing the smoothing processingon the basis of the real width Wreal, and calculates the relativedistance Z on the basis of the real width Wreal1 and the image widthWpic. Accordingly, in the vehicle exterior environment detectionapparatus 1, even in a case where fluctuations in the real widths Wrealoccur, the fluctuations are suppressed by performing the smoothingprocessing; therefore, it is possible to suppress the fluctuations inthe relative distances Z. As a result, the vehicle exterior environmentdetection apparatus 1 is able to enhance accuracy of detecting thepreceding vehicle 90.

Example Effects

As described above, in the example embodiment, in a case where thereliability of the image width is higher than the predeterminedthreshold, the real width Wreal is calculated on the basis of the imagewidth and the predicted relative distance, the real width Wreal1 isupdated by performing the smoothing processing on the basis of the realwidth Wreal, and the relative distance is calculated on the basis of thereal width Wreal1 and the image width. Therefore, it is possible toreduce the possibility that the image width that causes the predictedrelative speed to fluctuate influences the real width Wreal1. Thus, itis possible to enhance the accuracy of detecting the preceding vehicle.

In the example embodiment, the real width Wreal1 is updated byperforming the smoothing processing on the basis of the real widthWreal, and the relative distance is calculated on the basis of the realwidth Wreal1 and the image width. Therefore, even in a case wherefluctuations in the real widths Wreal occur, the fluctuations aresuppressed by performing the smoothing processing. Thus, it is possibleto enhance the accuracy of detecting the preceding vehicle.

Although some example embodiments of the technology have been describedin the foregoing, the technology is by no means limited to the exampleembodiments. Various changes and modifications may be made to anyembodiment without departing from the scope of the technology.

For example, although an example embodiment has been described above inwhich the preceding vehicle 90 traveling ahead of the vehicle 10 isregarded as the target of processing, the technology is not limitedthereto. Alternatively, for example, a vehicle traveling behind thevehicle 10 may be regarded as the target of processing. In this case,the stereo camera 11 may capture an image behind the vehicle 10.

The example effects described above are merely illustrative andnon-limiting. Any embodiment may achieve an effect other than theexample effects described above.

The processor 20 illustrated in FIG. 1 is implementable by circuitryincluding at least one semiconductor integrated circuit such as at leastone processor (e.g., a central processing unit (CPU)), at least oneapplication specific integrated circuit (ASIC), and/or at least onefield programmable gate array (FPGA). At least one processor isconfigurable, by reading instructions from at least one machine readablenon-transitory tangible medium, to perform all or a part of functions ofthe processor 20. Such a medium may take many forms, including, but notlimited to, any type of magnetic medium such as a hard disk, any type ofoptical medium such as a CD and a DVD, any type of semiconductor memory(i.e., semiconductor circuit) such as a volatile memory and anon-volatile memory. The volatile memory may include a DRAM and a SRAM,and the nonvolatile memory may include a ROM and a NVRAM. The ASIC is anintegrated circuit (IC) customized to perform, and the FPGA is anintegrated circuit designed to be configured after manufacturing inorder to perform, all or a part of the functions of the processor 20illustrated in FIG. 1.

The invention claimed is:
 1. A vehicle exterior environment detectionapparatus comprising: an image width calculator configured to calculatea first image width of a target vehicle on a basis of a first image, thefirst image being one of a left image and a right image; a predicteddistance calculator configured to calculate a first predicted distanceto the target vehicle on a basis of the first image width; and arelative distance calculator configured to calculate a first reliabilityof the first image width, and, when the first reliability is higher thana predetermined threshold, configured to calculate a first real width ofthe target vehicle on a basis of the first image width and the firstpredicted distance, update a smoothed real width by performing smoothingprocessing on a basis of the first real width, and calculate a firstdistance to the target vehicle on a basis of the smoothed real width andthe first image width, wherein the image width calculator is configuredto calculate a second image width of the target vehicle on a basis of asecond image, the second image being one of the left image and the rightimage and being captured at a timing later than a timing at which thefirst image is captured, the predicted distance calculator is configuredto calculate a second predicted distance to the target vehicle on abasis of the second image width, and the relative distance calculator isconfigured to calculate a second reliability of the second image width,and, when the second reliability is lower than the predeterminedthreshold, configured to calculate a second distance to the targetvehicle on a basis of the smoothed real width and the second imagewidth.
 2. The vehicle exterior environment detection apparatus accordingto claim 1, further comprising a relative speed calculator configured tocalculate, on a basis of the first distance and the second distance, arelative speed between a traveling speed of an own vehicle on which thevehicle exterior environment detection apparatus is to be mounted and atraveling speed of the target vehicle.
 3. The vehicle exteriorenvironment detection apparatus according to claim 1, wherein therelative distance calculator is configured to calculate the firstreliability, the first reliability of the first image width increasingas an own vehicle on which the vehicle exterior environment detectionapparatus is mounted comes closer to a position right behind the targetvehicle.
 4. The vehicle exterior environment detection apparatusaccording to claim 2, wherein the relative distance calculator isconfigured to calculate the first reliability, the first reliability ofthe first image width increasing as an own vehicle on which the vehicleexterior environment detection apparatus is mounted comes closer to aposition right behind the target vehicle.
 5. A vehicle exteriorenvironment detection apparatus comprising circuitry configured tocalculate a first image width of a target vehicle on a basis of a firstimage, the first image being one of a left image and a right image,calculate a first predicted distance to the target vehicle on a basis ofthe first image width, calculate a first reliability of the first imagewidth, and determine whether the first reliability is higher than apredetermined threshold, when the first reliability is higher than apredetermined threshold, the circuitry being configured to calculate afirst real width of the target vehicle on a basis of the first imagewidth and the first predicted distance, update a smoothed real width byperforming smoothing processing on a basis of the first real width, andcalculate a first distance to the target vehicle on a basis of thesmoothed real width and the first image width; calculate a second imagewidth of the target vehicle on a basis of a second image, the secondimage being one of the left image and the right image and being capturedat a timing later than a timing at which the first image is captured;calculate a second predicted distance to the target vehicle on a basisof the second image width; and calculate a second reliability of thesecond image width, and when the second reliability is lower than thepredetermined threshold, calculate a second distance to the targetvehicle on a basis of the smoothed real width and the second imagewidth.
 6. A vehicle exterior environment detection apparatus comprising:an image width calculator configured to calculate a first image width ofa target vehicle on a basis of a first image, the first image being oneof a left image and a right image; a predicted distance calculatorconfigured to calculate a first predicted distance to the target vehicleon a basis of the first image width; and a relative distance calculatorconfigured to calculate a first reliability of the first image width,and, when the first reliability is higher than a predeterminedthreshold, configured to calculate a first real width of the targetvehicle on a basis of the first image width and the first predicteddistance, update a smoothed real width by performing smoothingprocessing on a basis of the first real width, and calculate a firstdistance to the target vehicle on a basis of the smoothed real width andthe first image width, wherein the image width calculator is configuredto calculate a second image width of the target vehicle on a basis of asecond image, the second image being one of the left image and the rightimage and being captured at a timing later than a timing at which thefirst image is captured, the predicted distance calculator is configuredto calculate a second predicted distance to the target vehicle on abasis of the second image width, and the relative distance calculator isconfigured to calculate a second reliability of the second image width,and configured, when the second reliability is higher than thepredetermined threshold, to calculate a second real width of the targetvehicle on a basis of the second image width and the second predicteddistance, update the smoothed real width by performing the smoothingprocessing on a basis of the second real width, and calculate a seconddistance to the target vehicle on a basis of the smoothed real width andthe second image width.
 7. The vehicle exterior environment detectionapparatus according to claim 6, further comprising a relative speedcalculator configured to calculate, on a basis of the first distance andthe second distance, a relative speed between a traveling speed of anown vehicle on which the vehicle exterior environment detectionapparatus is to be mounted and a traveling speed of the target vehicle.8. The vehicle exterior environment detection apparatus according toclaim 7, wherein the relative distance calculator is configured tocalculate the first reliability, the first reliability of the firstimage width increasing as an own vehicle on which the vehicle exteriorenvironment detection apparatus is mounted comes closer to a positionright behind the target vehicle.
 9. The vehicle exterior environmentdetection apparatus according to claim 6, wherein the relative distancecalculator is configured to calculate the first reliability, the firstreliability of the first image width increasing as an own vehicle onwhich the vehicle exterior environment detection apparatus is mountedcomes closer to a position right behind the target vehicle.